In the elevator and escalator industry, equipment uptime and rapid service response directly impact safety and customer satisfaction. Traditional maintenance models often depend on static service schedules or customer-reported issues, leading to unnecessary downtime and inefficient technician routing. To overcome these challenges, the company required a scalable automation platform capable of analyzing IoT data in real time, predicting potential failures, and triggering proactive service interventions.
Challenge
Prior to Decisions, the company faced several critical challenges:
- Manual scheduling and reactive service models created delays.
- Underutilized IoT data prevented early detection of maintenance needs.
- Extended downtime due to slow issue identification and delayed technician response.
- Inefficient technician assignments drove up administrative overhead and slowed service resolution.
- Limited visibility into performance trends hindered efforts to scale predictive maintenance globally.
These limitations increased costs, reduced service quality, and weakened customer confidence.
Solution
The company deployed Decisions as the central rules engine for IoT-driven automation. The platform enabled real-time monitoring of 100,000 connected elevators worldwide by analyzing millions of heartbeat records daily. Key features included:
- Rules-driven anomaly detection to identify maintenance needs before failures occurred.
- Automated workflows to trigger maintenance actions instantly.